Split incoming text and return chunks.
class langchain.text_splitter.TextSplitter(chunk_size: int = 4000, chunk_overlap: int = 200, length_function: typing.Callable[[str], int] = <built-in function len>)[source]#
Interface for splitting text into chunks.
create_documents(texts: List[str], metadatas: Optional[List[dict]] = None) → List[langchain.docstore.document.Document][source]#
Create documents from a list of texts.
classmethod from_huggingface_tokenizer(tokenizer: Any, **kwargs: Any) → langchain.text_splitter.TextSplitter[source]#
Text splitter that uses HuggingFace tokenizer to count length.
classmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any) → langchain.text_splitter.TextSplitter[source]#
Text splitter that uses tiktoken encoder to count length.
split_documents(documents: List[langchain.docstore.document.Document]) → List[langchain.docstore.document.Document][source]#
Split documents.
abstract split_text(text: str) → List[str][source]#
Split text into multiple components.
class langchain.text_splitter.TokenTextSplitter(encoding_name: str = 'gpt2', allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any)[source]#
Implementation of splitting text that looks at tokens.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
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By Harrison Chase